Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning
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Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning. / Tummala, Sudhakar; Thadikemalla, Venkata Sainath Gupta; Kreilkamp, Barbara A.K.; Dam, Erik B.; Focke, Niels K.
In: Computers in Biology and Medicine, Vol. 139, 104997, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning
AU - Tummala, Sudhakar
AU - Thadikemalla, Venkata Sainath Gupta
AU - Kreilkamp, Barbara A.K.
AU - Dam, Erik B.
AU - Focke, Niels K.
PY - 2021
Y1 - 2021
N2 - BackgroundMagnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing.MethodA supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used.ResultsThe ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95–1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength).ConclusionsThe cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.
AB - BackgroundMagnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing.MethodA supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used.ResultsThe ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95–1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength).ConclusionsThe cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.
UR - https://doi.org/10.1016/j.compbiomed.2021.104997
U2 - 10.1016/j.compbiomed.2021.104997
DO - 10.1016/j.compbiomed.2021.104997
M3 - Journal article
C2 - 34753079
VL - 139
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
M1 - 104997
ER -
ID: 283001329